Tropical Atlantic Variability: Observations and Modeling

We review the state-of-the-art knowledge of Tropical Atlantic Variability (TAV). A well-developed observing system and sustained effort of the climate modeling community have improved our understanding of TAV. It is dominated by the seasonal cycle, for which some mechanisms have been identified. The...
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Autor*in:	William Cabos [verfasserIn] Alba de la Vara [verfasserIn] Shunya Koseki [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Tropical Atlantic Variability Tropical Atlantic climate sea-surface temperature biases observational data climate modeling

Übergeordnetes Werk:	In: Atmosphere - MDPI AG, 2011, 10(2019), 9, p 502
Übergeordnetes Werk:	volume:10 ; year:2019 ; number:9, p 502

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/atmos10090502

Katalog-ID:	DOAJ074967134

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520			\|a We review the state-of-the-art knowledge of Tropical Atlantic Variability (TAV). A well-developed observing system and sustained effort of the climate modeling community have improved our understanding of TAV. It is dominated by the seasonal cycle, for which some mechanisms have been identified. The interannual TAV presents a marked seasonality with three dominant modes: (i) the Atlantic Zonal Mode (AZM), (ii) the Atlantic Meridional Mode (AMM) and (iii) the variability in the Angola–Benguela Front (ABF). At longer time scales, the AMM is active and low-frequency variations in the strength, periodicity, and spatial structure of the AZM are observed. Also, changes in the mean position of the ABF occur. Climate models still show systematic biases in the simulated TAV. Their causes are model-dependent and relate to drawbacks in the physics of the models and to insufficient resolution of their atmospheric and oceanic components. The identified causes for the biases can have local or remote origin, involving the global ocean and atmospheric circulation. Although there is not a clear consensus regarding the role of model resolution in the representation of the TAV, eddy-resolving ocean models combined with atmospheric models with enhanced horizontal and vertical resolutions simulate smaller biases.
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allfieldsSound	10.3390/atmos10090502 doi (DE-627)DOAJ074967134 (DE-599)DOAJ05cc5fbfe8ee4fbcb131f758840fcb86 DE-627 ger DE-627 rakwb eng QC851-999 William Cabos verfasserin aut Tropical Atlantic Variability: Observations and Modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We review the state-of-the-art knowledge of Tropical Atlantic Variability (TAV). A well-developed observing system and sustained effort of the climate modeling community have improved our understanding of TAV. It is dominated by the seasonal cycle, for which some mechanisms have been identified. The interannual TAV presents a marked seasonality with three dominant modes: (i) the Atlantic Zonal Mode (AZM), (ii) the Atlantic Meridional Mode (AMM) and (iii) the variability in the Angola–Benguela Front (ABF). At longer time scales, the AMM is active and low-frequency variations in the strength, periodicity, and spatial structure of the AZM are observed. Also, changes in the mean position of the ABF occur. Climate models still show systematic biases in the simulated TAV. Their causes are model-dependent and relate to drawbacks in the physics of the models and to insufficient resolution of their atmospheric and oceanic components. The identified causes for the biases can have local or remote origin, involving the global ocean and atmospheric circulation. Although there is not a clear consensus regarding the role of model resolution in the representation of the TAV, eddy-resolving ocean models combined with atmospheric models with enhanced horizontal and vertical resolutions simulate smaller biases. Tropical Atlantic Variability Tropical Atlantic climate sea-surface temperature biases observational data climate modeling Meteorology. Climatology Alba de la Vara verfasserin aut Shunya Koseki verfasserin aut In Atmosphere MDPI AG, 2011 10(2019), 9, p 502 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:10 year:2019 number:9, p 502 https://doi.org/10.3390/atmos10090502 kostenfrei https://doaj.org/article/05cc5fbfe8ee4fbcb131f758840fcb86 kostenfrei https://www.mdpi.com/2073-4433/10/9/502 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019 9, p 502
language	English
source	In Atmosphere 10(2019), 9, p 502 volume:10 year:2019 number:9, p 502
sourceStr	In Atmosphere 10(2019), 9, p 502 volume:10 year:2019 number:9, p 502
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institution	findex.gbv.de
topic_facet	Tropical Atlantic Variability Tropical Atlantic climate sea-surface temperature biases observational data climate modeling Meteorology. Climatology
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container_title	Atmosphere
authorswithroles_txt_mv	William Cabos @@aut@@ Alba de la Vara @@aut@@ Shunya Koseki @@aut@@
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callnumber-first	Q - Science
author	William Cabos
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topic_title	QC851-999 Tropical Atlantic Variability: Observations and Modeling Tropical Atlantic Variability Tropical Atlantic climate sea-surface temperature biases observational data climate modeling
topic	misc QC851-999 misc Tropical Atlantic Variability misc Tropical Atlantic climate misc sea-surface temperature biases misc observational data misc climate modeling misc Meteorology. Climatology
topic_unstemmed	misc QC851-999 misc Tropical Atlantic Variability misc Tropical Atlantic climate misc sea-surface temperature biases misc observational data misc climate modeling misc Meteorology. Climatology
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title	Tropical Atlantic Variability: Observations and Modeling
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title_full	Tropical Atlantic Variability: Observations and Modeling
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title_sort	tropical atlantic variability: observations and modeling
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title_auth	Tropical Atlantic Variability: Observations and Modeling
abstract	We review the state-of-the-art knowledge of Tropical Atlantic Variability (TAV). A well-developed observing system and sustained effort of the climate modeling community have improved our understanding of TAV. It is dominated by the seasonal cycle, for which some mechanisms have been identified. The interannual TAV presents a marked seasonality with three dominant modes: (i) the Atlantic Zonal Mode (AZM), (ii) the Atlantic Meridional Mode (AMM) and (iii) the variability in the Angola–Benguela Front (ABF). At longer time scales, the AMM is active and low-frequency variations in the strength, periodicity, and spatial structure of the AZM are observed. Also, changes in the mean position of the ABF occur. Climate models still show systematic biases in the simulated TAV. Their causes are model-dependent and relate to drawbacks in the physics of the models and to insufficient resolution of their atmospheric and oceanic components. The identified causes for the biases can have local or remote origin, involving the global ocean and atmospheric circulation. Although there is not a clear consensus regarding the role of model resolution in the representation of the TAV, eddy-resolving ocean models combined with atmospheric models with enhanced horizontal and vertical resolutions simulate smaller biases.
abstractGer	We review the state-of-the-art knowledge of Tropical Atlantic Variability (TAV). A well-developed observing system and sustained effort of the climate modeling community have improved our understanding of TAV. It is dominated by the seasonal cycle, for which some mechanisms have been identified. The interannual TAV presents a marked seasonality with three dominant modes: (i) the Atlantic Zonal Mode (AZM), (ii) the Atlantic Meridional Mode (AMM) and (iii) the variability in the Angola–Benguela Front (ABF). At longer time scales, the AMM is active and low-frequency variations in the strength, periodicity, and spatial structure of the AZM are observed. Also, changes in the mean position of the ABF occur. Climate models still show systematic biases in the simulated TAV. Their causes are model-dependent and relate to drawbacks in the physics of the models and to insufficient resolution of their atmospheric and oceanic components. The identified causes for the biases can have local or remote origin, involving the global ocean and atmospheric circulation. Although there is not a clear consensus regarding the role of model resolution in the representation of the TAV, eddy-resolving ocean models combined with atmospheric models with enhanced horizontal and vertical resolutions simulate smaller biases.
abstract_unstemmed	We review the state-of-the-art knowledge of Tropical Atlantic Variability (TAV). A well-developed observing system and sustained effort of the climate modeling community have improved our understanding of TAV. It is dominated by the seasonal cycle, for which some mechanisms have been identified. The interannual TAV presents a marked seasonality with three dominant modes: (i) the Atlantic Zonal Mode (AZM), (ii) the Atlantic Meridional Mode (AMM) and (iii) the variability in the Angola–Benguela Front (ABF). At longer time scales, the AMM is active and low-frequency variations in the strength, periodicity, and spatial structure of the AZM are observed. Also, changes in the mean position of the ABF occur. Climate models still show systematic biases in the simulated TAV. Their causes are model-dependent and relate to drawbacks in the physics of the models and to insufficient resolution of their atmospheric and oceanic components. The identified causes for the biases can have local or remote origin, involving the global ocean and atmospheric circulation. Although there is not a clear consensus regarding the role of model resolution in the representation of the TAV, eddy-resolving ocean models combined with atmospheric models with enhanced horizontal and vertical resolutions simulate smaller biases.
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title_short	Tropical Atlantic Variability: Observations and Modeling
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Tropical Atlantic Variability: Observations and Modeling

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